• A data visualization package in R
• Builds graphics declaratively using layers in the spirit of ggplot2
• Produces graphics rendered in a web browser using Vega, a visualization grammar built on top of D3
• Produces interactive graphics leveraging the power of Shiny

ggplot(mtcars, aes(x=wt, y=mpg))+
  geom_point()

mtcars %>% 
  ggvis(x= ~wt, y= ~mpg) %>% 
  layer_points()

mtcars %>% 
  ggvis(x= ~wt, y= ~mpg) %>% 
  layer_points() %>% layer_smooths()

• ggvis uses a function interface, so chaining is done using %>% rather than +
• ggvis uses x = ~wt instead of x = wt (will come back to this)
• Using ggvis() without any layers is akin to qplot

<data>  %>% 
  ggvis(~<x property>,~<y property>, 
        fill = ~<fill property>, ...) %>% 
  layer_<marks>()`

<marks> can be arcs, bars, densities, freqpolys, histograms, images, lines, paths, model_predictions, points, rects, ribbons, smooths, text

faithful %>% 
    ggvis(~waiting, ~eruptions, fill := "red") %>% 
    layer_points() %>% 
    add_axis("y", title = "Duration of eruption (m)", 
             values = c(2, 3, 4, 5), subdivide = 9) %>% 
    add_axis("x", title = "Time since previous eruption (m)")

• Data
• A coordinate system
• Marks
• Corresponding properties

• = maps a property to a data value or a set of data values
• := sets a property to a specific value

pressure %>% ggvis(~temperature, ~pressure, fill = "red") %>% 
  layer_points()

• = maps a property to a data value or a set of data values
• := sets a property to a specific value

pressure %>% ggvis(~temperature, ~pressure, fill := "red") %>% 
  layer_points()

See http://ggvis.rstudio.com/properties-scales.html for more details.

ggvis code accepts three types of objects:

• A string of letters, without quotes, refers to an object name, and will be searched in the current environment
• A tilde ~ at the start of the string will be treated as a variable name, and will be searched in the current data frame
• A string in quotes will be treated as a raw value.

shade <- 'red'
mtcars %>% ggvis(~disp, ~mpg) %>% 
  layer_points(fill= ~factor(cyl), size :=300, stroke := shade, 
               strokeWidth :=6) %>% add_legend('fill',title='Cylinders')

mtcars %>% group_by(am) %>% 
  ggvis(~mpg, ~hp) %>% layer_smooths(stroke = ~factor(am)) %>% 
    layer_points(fill = ~factor(am))

mtcars %>% group_by(am) %>% 
  ggvis(~mpg, ~hp) %>% layer_smooths(stroke = ~factor(am)) %>% 
    layer_points(fill = ~factor(am)) %>% 
  scale_nominal('fill', range=c("green","orange")) %>% 
  scale_nominal('stroke', range=c('green','orange'))

base <- mtcars %>% ggvis(~mpg, ~cyl) %>% layer_points()
base %>% group_by(cyl) %>% summarise(mpg = mean(mpg)) %>%
  layer_points(fill := "red", size := 100)

mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>% 
 layer_smooths(se=T,
   span=input_slider(0.1,0.9,value=0.75,step=0.05,label='span'))

## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

mtcars %>% mutate(name=row.names(mtcars)) %>% 
  ggvis(~wt, ~mpg, key := ~name) %>% layer_points() %>% 
  add_tooltip(function(df) df$name)

mtcars %>% ggvis(~wt, ~mpg) %>%
  layer_points() %>%
  layer_model_predictions(model = "lm", stroke:='black') %>%
  layer_model_predictions(model = "loess", stroke := "red") 

• ggvis.rstudio.com
• Tutorial
• Additional examples
• Interactivity
• Customization